European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 1 www.idpublications.org
THE DETERMINANTS OF CAPITAL STRUCTURE: EMPIRICAL EVIDENCE
FROM KUWAIT
Ahmad Mohammad Obeid Gharaibeh
Assistant Professor, Dept. of Banking and Finance, Ahlia University
KINGDOM OF BAHRAIN
ABSTRACT
The main purpose of this study is to examine the determinants of capital structure of a firm.
Capital structure is encapsulated by total liabilities to total assets. The study provides further
empirical evidence of the capital structure theories pertaining to developing countries by
examining the impact of certain measures on the decision to finance the firm. The panel data
used was obtained from financial statements and annual reports of the study sample
comprised of 49 industrial and service firms out of the 215 companies listed in the Kuwait
stock exchange. The investigation was performed using 6 years data for the period from 2009
to 2013. Multiple regressions represented by ordinary least squares (OLS) were used to
examine the factors determining the capital structure. The results of the cross-sectional OLS
regression show that growth opportunity, firms’ age, liquidity, profitability, size, tangibility,
and industry type have statistically significant relationship with firm’s leverage. Dividends
policy and ownership structure of the firm, however, were found to have negative but
statistically insignificant relationships with capital structure. Accordingly, the findings of the
study reveal that firm’s age, growth opportunities, liquidity, profitability, firm’s size,
tangibility, and type of industry are determinants of capital structure of firms listed in
Kuwaiti stock exchange (KSE). Dividends policy and ownership structure, however, are
revealed to be non-determinants of capital structure.
Keywords: capital structure, multiple regression, OLS, total liabilities to total assets, growth
opportunity, firm’s age, liquidity, profitability, size, tangibility, industry type, dividends
policy, ownership structure.
INTRODUCTION
This study intends to investigate the factors that affect the capital structure decision of the
firm. Capital structure is composed of a combination of debt (short and long term) and equity
(common and preferred stocks).The optimal capital structure issue has been debated by many
scholars and researchers for several decades. The capital structure theory was first developed
by Modigliani and Miller in 1958. Rajan and Zinglales (1995) and Gill et al., (2009) pointed
out that the empirical evidence of successive theories of capital structure is still far from
conclusive. The question of how to choose the capital structure of a firm is still unanswered
despite the extensive researches that have been conducted in this regard.
Factors affecting capital structure differ from one country to the other due to differences in
the level of social, environmental, economic, technological and cultural development (Mazur,
2007). Doug S. (2014) pointed out that different studies have suggested that financial
decisions in developing countries are somehow different from those of developed ones
because of their institutional differences such as level of transparency and investor protection,
besides the bankruptcy and tax laws. Consequently, research findings from one country
cannot be generalized to other countries. This recommends a need for country specific
studies.
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Research Problem
Financial managers in addition to other stakeholders of all firms around the world believably
will want to know the proper capital structure (mix of debt and equity) that maximizes a
firm’s value. To be able to know the proper mix capital structure, firms may want to know
the factors that influence that capital structure (i.e., the determinants of capital structure).
Accordingly, financial managers will principally want to know the relationships between
certain firm-specific measures and the debt ratio. They possibly will need to know the factors
that influence the capital structure of their firms. In that case, financial managers (policy
makers) need to primarily know the impacts of changing certain firm-specific measures on
the capital structure of the firm. Primarily, they need to identify the relationships between
certain measures like profitability, liquidity, growth opportunity, dividend policies,
ownership structure, tangibility, riskiness, etc. on financing decisions of the firm. The
outcomes of this study will probably make a contribution to the body of literature governing
finance decisions in this milieu.
Objectives of the Study
The main objective of this study is to identify the determinants of leveraging (capital
structure). The study seeks to statistically measure the relationships between capital structure
and certain firm specific measures of companies listed in the Kuwaiti stock exchange.
Specifically, it intends to measure the relationships between capital structure and each of
growth opportunity, dividends policy, age, liquidity, profitability, size, tangibility, industry
type, and riskiness of the firm. Eventually, the study will identify the determinants of capital
structure based on data obtained from a number of companies listed in the Kuwait Stock
Exchange.
Significance of the Study
Published findings based on data from developing countries have not appeared until recently
(Booth et al., 2001 and Huang and Song, 2002). So far, no study has been published based on
data from GCC countries at least to the extent of the researcher’s cognizance. Therefore, the
main goal of this paper is to bridge this gap, probing the case of the Kuwaiti firms.
Moreover, the study focuses on measuring the relationships between leverage and changes
(variations) in the independent variables (the regressand). This study contributes to the
literature in that it is of the pioneer studies conducted in an emerging market in this important
part of the world (Kuwait). Kuwait stock exchange is co-integrated with other GCC stock
exchanges. Ibrahim Onour (2009) observed strong evidence of co-integration between five
GCC stock markets. He found a bivariate non-linear co-integrating relationship linking the
Kuwait stock exchange with each of Saudi and Dubai exchanges. He observed the existence
of non-linearity between Saudi stock market and each of Dubai and Abu-Dhabi stock markets
and between Muscat and Kuwait stock exchanges.
This study is conducted based on data obtained not only from one sector, as most of the
published studies do, but also based on data obtained from multiple sectors. This will provide
evidence on the impact of industry on capital structure factors.
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LITERATURE REVIEW AND THE EMPIRICAL EVIDENCE OF THE
DETERMINANTS OF CAPITAL STRUCTURE
Capital structure studies (theoretical or empirical) have produced many findings that
endeavor to explain the determinants of capital structure. The trade-off theory (also referred
to as the tax based theory) states that capital structure is determined by a trade-off between
the benefits of debt (tax savings) and the costs of debt (liquidation and bankruptcy). In that
sense, then, firms ought to balance the tax benefits of debt against the burden costs of
liquidation or bankruptcy. The agency perspective of this theory is that debt disciplines
managers and lessens agency problems of free cash flow since debt must be repaid to avoid
bankruptcy (Jensen and Meckling, 1976; Jensen, 1986). Although debt lessens shareholder-
manager conflicts, it worsens shareholder-debt-holder conflicts (Stulz, 1990).
Kim and Sorensen (1986) tested the presence of the agency costs and their relation to the debt
policy of corporations. They find that firms with higher insider ownership have greater debt
ratios than firms with lower insider ownership, which may be explained by the agency costs
of debt and/or the agency costs of equity. Kim and Sorensen (1986) found that high-growth
firms use less debt rather than more debt, high-operating-risk firms use more debt rather than
less debt, and firm size appears to be uncorrelated to the level of debt.
Pecking order theory (also referred to as the information asymmetry theory) articulated by
Myers (1984) considers three sources of funds available to firms; retained earnings, debt, and
equity. It states that firms prefer to finance new investments firstly using available retained
earnings, then using debt, and finally by issuing new equity. This theory suggests a
relationship exists between profitability and capital structures as profitable firms are inclined
to rely more on retained earnings financing. Allen (1991) finds that companies appear to
follow a pecking order with respect to funding sources and also report policies of maintaining
spare debt capacity. Frank and Goyal (2004), in their study of US firms Capital structure
decisions from 1950 to 2000 pointed out that firms that have more collateral, more
competition or are large tend to have high leverage. Besides, they concluded that firms that
have more profits, or those pay dividend tend to have less leverage.
López-Gracia and Sánchez-Andújar (2007) confirm that a business's family nature does lead
it to employ financial policy different from the rest of businesses. Furthermore, they indicate
that financial distress costs, growth opportunities, and internal resources are the main factors
that differentiate the financial behavior of family firms from their nonfamily counterparts.
The size of the firm and its growth opportunities may influence the capital structure of the
firm. Chingfu et.al., (2009) found that growth is the most important determinant of capital
structure choice, followed in order by profitability, collateral value, volatility, non-debt tax
shields, and uniqueness.
Alberto and Pindado (2001), analyze characteristics of the firm that determine capital
structure according to different explanatory theories. They developed a target adjustment
model, which has then been confirmed by their empirical evidence. It highlights the fact that
the transaction costs borne by Spanish firms are inferior to those borne by US firms. Their
results were tandem with tax and financial distress theories. They also provide supplementary
evidence on the pecking order and free cash flow theories. Finally, the evidence they obtained
ascertained the impact of some institutional characteristics on capital structure.
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Huang and Song (2006) employ a new database containing data (from 1994 to 2003) from
more than 1200 Chinese-listed companies to document their capital structure characteristics.
They found that leverage in Chinese firms increases with firm size and fixed assets, and
decreases with profitability, non-debt tax shields, growth opportunity, managerial
shareholdings and correlates with industries. They confirmed that state ownership or
institutional ownership has no significant impact on capital structure and Chinese companies
consider tax effect in long-term debt financing. They concluded that Chinese firms tend to
have much lower long-term debt than other countries firms.
Joshua Abor (2008) in a study that compares the capital structures of different sizes of firms
in Ghana show that quoted and large unquoted firms exhibit significantly higher debt ratios
than do SMEs. His results indicate that age, size, asset structure, profitability, risk and
managerial ownership of the firm are important in influencing the capital structure decisions.
For the SME sample, he found that gender of the entrepreneur, export status, industry,
location and form of business are also important in explaining the capital structure choice.
Industry was found to be important in explaining the SMEs’ capital structure. Limited
liability companies, according to the author, are more likely to obtain long-term debt finance
relative to sole-proprietorship businesses.
Saumitra Bhaduri (2010) studies the capital structure choice of LDCs using a case study of
the Indian Corporate sector. He confirms that the optimal capital structure choice is
influenced by factors such as growth, cash flow, size, product and industry characteristics.
His results proposed the existence of restructuring costs in attaining an optimal capital
structure. Noulasa and Genimakis (2011) investigate the capital structure determination of
firms listed on the Athens Stock Exchange, using both cross-sectional and nonparametric
statistics. The first part of their study assesses the extent to which leverage depends upon a
broader set of capital structure determinants, while the latter provides evidence that capital
structure varies significantly across a series of firm classifications. Their results document
empirical regularities with respect to alternative measures of debt that are consistent with
existing theories. Particularly, their results support the pecking order hypothesis.
Aimed to test various hypotheses concerning the determinants of SME capital structure of
3500 unquoted, UK small and medium sized enterprises (SMEs), Graham et al. (2010)
establish that long-term debt was positively related to asset structure and company size and
negatively to age. Short-term debt, on the other hand, was found to be negatively related to
profitability, asset structure, size and age and positively to growth. Significant variation
across industries was found in most of the explanatory variables. Profitability was found to
have no effect on long-term borrowing in any industry.
Natalya Delcoure (2007) investigates whether capital structure determinants in emerging
Central and Eastern European (CEE) countries support traditional capital structure theory
developed to explain western economies. Her study suggested that some traditional capital
structure theories were portable to companies in CEE countries. She found on the other hand,
that neither the trade-off, pecking order, nor agency costs theories explain the capital
structure choices and companies follow the modified “pecking order.” The factors that
influence firms’ financing decision were found to be the differences and financial constraints
of banking systems, disparity in legal systems governing firms' operations, shareholders, and
bondholder’s rights protection, sophistication of equity and bond markets, in addition to
corporate governance.
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Miguel et al. (2014) analyze country-specific differences and how they influence capital
structure indirectly through firm-specific variables. They apply a system Generalized Method
of Moments technique to a panel data sample of companies from France, Germany, Italy,
Spain and the United Kingdom for the period 1998 to 2008. They concluded that there are
substantial differences in the capital structure choices of firms across five major European
countries. The differences, according to them, are motivated by the type of financial systems
of the countries. Their results support the relevance of the differences in the capital structure
choices of firms.
Doug S. Choi (2014) indicates that the financing decisions of the Korean firms can be
explained by the determinants suggested by the typical corporate finance models. He used a
regression model which employed profitability, tangibility of assets, industry types, firm size,
business risk, growth opportunities, tax shield substitutes, and corporate taxes as independent
variables that may explain the financial leverage. His results indicate that profitability,
tangibility of assets and firm size are significantly positively related to the financial leverage.
Growth opportunities and tax shield substitutes, in contrast, are found to be significantly
negatively related to the financial leverage. Surprisingly, depreciation charges as a percent of
total asset was found to be the most significant explanatory variable. This relationship
emphasizes the importance of tax shield substitutes for the firms in his sample.
SAMPLE AND METHODOLOGY
The study employs panel data regression analysis. Panel data is advantageous to period
average cross-sectional data. The panel data is used as the efficiency of economic estimates is
improved. It increases the degrees of freedom and reduces the collinearity among the
explanatory variables (Baltagi, 1995 and Gathogo and Ragui, 2014).The data set used for
empirical analysis was collected from the published annual reports of 49 firms listed in the
Kuwait stock exchange. Some other financial data were obtained from information published
by Kuwait Stock Exchange (KSE). The actual and historical financial data obtained embraces
financial figures of 49 industrial and service firms listed in the KSE. The 49 firms represent
the sample of the study chosen from a population of 215 firms including non-Kuwaiti listed
companies. A total of 284 adjusted observations were collected for analysis covering six
years period from 2008 to 2013.
The study sample was selected from multiple sectors including manufacturing, services, oil
and gas, and basic materials. All manufacturing (industrial) firms were included in the
sample. Other firms (nonmanufacturing) were selected randomly and based on the
availability of data. Some sectors (financial, real estate, and communications) were excluded
from the analysis as they are considered as either highly leveraged or having special
characteristics. This is, of course, to increase the reliability and avoid the sampling error
resulted from mixing all the listed firms.
The Study Hypotheses
Capital Structure is defined by Investopedia as the mix of a company's long-term debt,
specific short-term debt, common equity and preferred equity1. Equity comes in the form of
common stock, preferred stock and retained earnings, while debt is classified as bond issues
or long term notes payable. Short-term loans are considered as part of the capital structure of
1 http://www.investopedia.com/terms/c/capitalstructure.asp#ixzz3XO5HE4Kd
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the firm. The capital structure denotes to how a firm finances its operations and its growth
using various sources of funds. When people refer to capital structure they are most likely
referring to a firm's debt-to-equity ratio, which provides insight into how risky a company is
(Wikipedia)2. The firm's ratio of debt to total financing is referred to as the firm's leverage
(Fernandes, 2014). Thus, Leverage (gearing) represents the percentage of the firm's capital
that is financed through debt (bonds and bank loans).
Based on the above discussions and in order to explore the determinants of capital structure
(leveraging) the following alternative hypotheses are formulated and used for testing:
H1: There is a statistically significant relationship between use of debt and growth
opportunities of the firm.
H2: There is a statistically significant relationship between use of debt and dividend policy of
the firm.
H3: There is a statistically significant relationship between use of debt and age of the firm.
H4: There is a statistically significant relationship between use of debt and liquidity of the
firm.
H5: There is a statistically significant relationship between use of debt and ownership
structure of the firm.
H6: There is a statistically significant relationship between use of debt and profitability of the
firm.
H7: There is a statistically significant relationship between use of debt and size of the firm.
H8: There is a statistically significant relationship between use of debt and tangibility of
assets of the firm.
H9: There is a statistically significant relationship between use of debt and type of industry of
the firm.
H10: There is a statistically significant relationship between use of debt and business risk of
the firm.
The Study Model:
This study uses multiple-regression model for the estimation of a panel data. The obtained
data is analyzed through OLS regression. A panel data approach is more useful than either
cross-section or time-series data alone (Joshua Abor, 2008). It is used as the degrees of
freedom can increase and the collinearity of the explanatory factors can be reduced, and thus
the efficiency of the estimates can be improved. One period lagged leverage for the
regression is used in order to perceive the effects of the determinants after being known to the
company managers.
This research attempts to examine the determinants of capital structure using a correlating
test of both dependent and independent variables, and a multiple regression analysis of the
data set. A multiple regression model is employed since the study has more than one
independent variable. This study investigates the effect of 10 explanatory (independent)
variables on capital structure in order to determine the factors that influence the firm’s choice
of its capital structure. The variables used in this study were determined according to the
results reached by previous studies and based on the availability of the data for measurement
purposes. The current study examined whether growth opportunities, dividends policy, age,
liquidity, ownership structure, profitability, size, tangibility, industry type, and business risk
are significant determinants of capital structure.
2 http://en.wikipedia.org/capital structure
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The dependent variable (regressand) used is the capital structure of the firm measured by debt
ratio or (leverage) and is proxied by the ratio total liabilities to total assets3. The hypothesized
independent variables include growth opportunities of the firm (GROP) measured by price
per share to book value per share (P/BV)4, Dividend policy (DIVD) measured by the ratio of
dividends to net profit, firm’s age (FAGE) measured by number of years in business of the
firm, liquidity (LIQD) measured by total liability to total equity ratio, ownership structure
(OWNS) measured by a dummy variable where 1 denotes closely held companies and 0
denotes publicly held companies, profitability of the firm (PRFT) measured by return on
equity ratio (ROE) or net income divided by stockholders’ equity, size of the firm (SIZE)
measured by natural logarithm of total assets, tangibility of assets (TANG) measured by the
ratio of fixed assets to total assets, type of industry (TYPE) denoted by dummy variables
where 0 signifies industrial (manufacturing) and 1 signifies otherwise, and business risk of
the firm (RISK) measured by the standard deviations of ROE of the firm5.
Following is the multiple regression model estimated to test the above-mentioned hypotheses
of the study:
LEVR i, t = β0 + β1 GROP i, t + β2 DIVD i, t + β3 FAGE i, t + β4 LIQD i, t + β5 OWNS i, t + β6
PRFT i, t + β7 SIZE i, t + β8 TANG i, t + β9 TYPE i, t + β10 RISK i, t + ε
Where:
LEVR i, t = leverage or debt ratio of firm i in time t
Β0: the intercept or constant amount,
Β 1- β10 = coefficients of the explanatory variables.
GROP i, t = growth opportunities of firm i in time t
DIVD i, t = dividend policy of firm i in time t
FAGE i, t = age of firm i in time t
LIQD i, t = asset liquidity of firm i in time t
OWNS i, t, = ownership structure for firm i in time t
PRFT i, t = profitability of firm i in time t
SIZE i, t = size of firm i in time t
TANG i, t = Tangibility of assets for firm i in time t
TYPE i, t = Industry type of firm i in time t
RISK i, t = Business risk of firm i in time t
ε: the error term.
RESEARCH RESULTS AND DISCUSSION
The following sections represent the results of the study. Besides the descriptive statistics, the
results include the correlation analysis and regression analysis.
Descriptive statistics
The analysis of the results starts with a range of descriptive statistics. Table 1 below
represents the descriptive statistics of the dependent as well as independent variables of the
3 This is the broadest definition of leverage and used as proxy for what is left for shareholders in case of
liquidation (Rajan and Zingales, 1995). 4 The use of this ratio is analogous to the judgments of Fama and French’s (1992) book-to-market ratio which
show that the B value/M value (or its reciprocal) of individual stocks can explain cross sectional variation in stock returns. This ratio is commonly used as a measure of a firm’s growth opportunities. 5 Standard deviation of return on assets is used as a proxy for volatility or risk by many authors for example:
Patrik Bauer (2004).
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study. It illustrates the mean, median, standard deviations, maximum, and minimum values of
the 288 observations related to the 49 firms included in the study. The average leverage ratio
as can be seen in the Table equals 36.988%. Dividend payout (policy) accounted for
0.367166, firms age 26.82988, growth opportunities 1.129046, liquidity 1.565551, ownership
structure 0.315353, profitability 0.064259, business risk 0.092114, size 11.12285, tangibility
0.279265, and industry type 0.398340. The maximum leverage ratio is 0.993296 and the
minimum leverage ratio is 0.009036, whereas the standard deviation of leverage is 0.223314.
The results of minimum value range from -9.400000 to 8.522380, and the results of
maximum value range from 1.0000 to 148.1737.
The low standard deviations figures for many variables indicate that most of the firms are in
the same range of leverage, dividends payout, growth opportunities, ownership structure,
profitability, riskiness, tangibility and type. Positive and negative values of skewness indicate
that the outcomes, to a certain degree, are not normally distributed.
Table (1): Descriptive statistics LEVR DIVD FAGE GROP LIQD OWNS PRFT RISK SIZE TANG TYPE
Mean 0.36506 0.36717 26.8299 1.12905 1.56555 0.31535 0.06426 0.09211 11.1229 0.27927 0.39834
Median 0.32200 0.33100 30.0000 1.00000 0.46900 0.00000 0.05270 0.03097 11.1297 0.22212 0.00000
Maximum 0.99330 2.46000 53.0000 4.90000 148.174 1.00000 7.80810 5.84664 14.3945 1.01211 1.00000
Minimum 0.00904 -6.09800 5.00000 -9.40000 0.00912 0.00000 -0.86600 0.00000 8.52238 0.000001 0.00000
Std. Dev. 0.223314 0.63459 11.6304 0.97450 9.56489 0.46562 0.52493 0.38406 1.27396 0.244712 0.49058
Skewness 0.42972 -3.67357 -0.12827 -4.54438 15.0399 0.79477 13.3312 14.0703 0.15607 0.82042 0.41531
Correlation Analysis
The correlation coefficient is used in this study as a method to explore the type and intensity
of the relationships among all the variables being dependent or independent. Table 2 below
displays the correlations matrix of the proxy variables used in this study. The correlation
matrix is used here to test the degree of multicollinearity among the variables (regressor) of
the study sample. Liquidity and profitability were shown to have the highest positive
correlations between them (0.93809). The high degree of the correlation coefficients between
liquidity and profitability indicates the presence of multicollinearity. This means that one of
these variables can be dropped from the study model. Risk is found to be highly correlated
with liquidity (0.96633) and profitability (0.89223). This means the existence of
multicollinearity between risk and these two variables. Therefore, risk is excluded from
further analysis. Remarkable positive correlations were also found between firm’s age and
ownership structure, age and size, size and industry type, and between profitability and
tangibility of assets. Yet, the Table displays a remarkably negative correlation between
profitability and growth opportunity of the firm.
The correlation test is also used to determine the most significant factors in the list of
hypothesized independent variables (Gathogo and Ragui, 2014). The most significant positive
correlation between leverage and the independent variables, according to the Table, appear to
be firm’s age, liquidity, risk, size, and industry type. Next in strength comes profitability and
tangibility of the firm. Dividends policy seems to have the highest negative correlation with
leverage. Nevertheless, growth opportunities, ownership structure, and tangibility appear to
have the lowest significant correlation with the leverage of the firm.
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Table (2): Correlation Matrix LEVR DIVD FAGE GROP LIQD OWNS PRFT RISK SIZE TANG TYPE
LEVR 1.00000
DIVD -0.18906 1.00000
FAGE 0.19865 -0.00313 1.00000
GROP -0.02998 0.11174 0.03286 1.00000
LIQD 0.28559 -0.05735 0.14442 -0.67430 1.00000
OWNS -0.08575 0.02681 0.30540 0.12298 -0.04767 1.00000
PRFT 0.13376 0.04456 0.11931 -0.58594 0.93809 -0.01430 1.00000
RISK 0.20459 -0.08444 0.09906 -0.69428 0.96633 -0.05632 0.89223 1.00000
SIZE 0.42871 0.01267 0.20766 -0.08269 0.10009 -0.08650 0.07406 0.02984 1.00000
TANG 0.04289 0.08346 0.13312 0.00423 0.07654 0.03400 0.11032 0.05284 -0.08369 1.00000
TYPE 0.43897 -0.10335 -0.12391 0.09423 -0.01839 -0.07795 -0.05797 -0.02798 0.16538 -0.02771 1.00000
Regression Analysis
This study uses multiple regression analysis to identify the determinants of capital structure
of the selected study sample of listed firms in Kuwait Stock Exchange (KSE). Durbin-Watson
statistics, adjusted R-square, and Prob. value were used in this study for decision making
criteria. P-value is used here in this study as criteria to help decide whether to accept or to
reject the proposed hypothesis. A P-value of less than or equal to 1% means that the null
hypothesis is rejected at 1% level of significance. A p-value of less than or equal to 5%
means that the null hypothesis is rejected at 5% level of significance. A p-value of less than
or equal to 10% means that the null hypothesis is rejected at 10% level of significance.
Rejecting the null hypotheses certainly means accepting the alternative ones.
The Adjusted R-square is used in multiple regression analysis to measure the goodness-of-fit
that penalizes additional explanatory variables by using a degrees of freedom adjustment
when estimating the variance error. The adjusted R-squared of 0.768858 designates that
variations in the hypothesized independent variables can explain the variations in the
dependent variables by 76.88%. Accordingly, drawn conclusions can be considered as
reliably supported by the data.
Durbin-Watson (D-W) is used to test for first order serial correlation in the errors of a
regression model (Tony Lancaster, 2004). D-W statistics helps in identifying the right
combination of explanatory variables. Durbin and Watson applied this statistic to the
residuals from ordinary least squares (OLS) regressions and developed bounds tests for
the null hypothesis that the errors are serially uncorrelated. D-W Statistic is also used to test
the presence of autocorrelation in the residuals (prediction errors). The D-W statistic of
2.052369 indicates an absence of autocorrelation. It implies neither overestimation nor
underestimation of the level of significance for such a size of observations.
Table 3 below represents the regression results of the study variables. It shows the regression
analysis between leverage (LEVR) on the one hand and dividend policy (DIVD), growth
opportunities (GROP), liquidity (LIQD), ownership structure (OWNS), profitability (PRFT),
size (SIZE), tangibility of assets (TANG) and industry type (TYPE) on the other.
The Table shows a significant positive relationship exists between leverage and changes in
growth opportunities of the firm with level of significance at 1% and a p-value of (0.0043).
This suggests that an increase in the growth opportunities of the firm increases the demand
for debt financing. In other words, firms tend to borrow when growth opportunities increases.
Therefore, the First hypothesis that there is a statistically significant relationship between
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capital structure and growth opportunities of the firm is accepted. This result is consistent
with the research results of Michaelas et al. (1999), Cassar and Holmes (2003), Hall et al.
(2004) and Yasir Bin Tariq (2006) who showed positive associations between growth and
debt ratio of the firm. However, it is not consistent with the results of Kim and Sorensen
(1986), Stulz, (1990), Rajan and Zingales (1995), Roden and Lewellen (1995), and Al-Sakran
(2001).
Table (3) Regression analysis between leverage (LEVR) and the independent variables
Variable Coefficient Std. Error t-Statistic Prob.
C 0.017371 0.018583 0.934791 0.3507
LEVR(-1) 0.841942 0.034406 24.47078 0.0000***
D(DIVD) -0.001241 0.001342 -0.924865 0.3559
FAGE 0.001203 0.000620 1.938519 0.0536*
D(GROP) 0.035361 0.012284 2.878723 0.0043***
D(LIQD) 0.016822 0.002201 7.642067 0.0000***
OWNS -0.012345 0.014850 -0.831319 0.4065
D(PRFT) -0.212913 0.033116 -6.429294 0.0000***
D(SIZE) 0.068285 0.009277 7.360675 0.0000***
D(TANG) 0.104049 0.027835 3.738092 0.0002***
TYPE 0.030304 0.015297 1.980992 0.0486**
R-squared 0.777025 Mean dependent var 0.369168
Adjusted R-squared 0.768858 S.D. dependent var 0.225785
S.E. of regression 0.108551 Akaike info criterion -1.565228
Sum squared resid 3.216855 Schwarz criterion -1.423895
Log likelihood 233.2624 Hannan-Quinn criter. -1.508565
F-statistic 95.13551 Durbin-Watson stat 2.052369
Prob(F-statistic) 0.000000
***, **, and *, signify 1%, 5% and 10% respectively.
Table (3) also shows that the lagged leverage ratio (LEVR (-1)) coefficient of 0.841942 is
positive and statistically significant at 1% level of significance (p = 0.0000). This significant
relationship implies that previous years leverage explains the current year’s leverage.
The results revealed that there is a negative but statistically insignificant relationship between
changes in dividends policy of the firm and its debt ratio. This can be explained by the fact
that firms with high dividend payments are liquid enough to finance their growth internally.
The insignificance of this result may imply that dividend policy is not a determinant factor of
the capital structure of the firm. Therefore, the Second hypothesis that there is a statistically
significant relationship between capital structure and dividend policy of the firm is rejected.
This result is consistent with the research results of Saurabh and Sharma (2015) who found
that dividend payout to be empirically insignificant to determine the capital structure of
Indian manufacturing sector. However, the result is inconsistent with that of Frank and
Goyal (2004) who concluded that firms that pay dividend tend to have less leverage.
In addition, firm’s age is found to have a statistically significant positive relationship at 10%
level of significance with p-value of 0.0536. This significant positive relationship implies that
debt ratio increases as the age of the firm increases. This can probably be explained by the
fact that the longer histories on the stock market imply better monitoring from the banks, and
thus a reduction of the agency costs of in case of debt finance. Therefore the Third hypothesis
that there is a statistically significant relationship between capital structure and age of the
firm is accepted. This result is consistent with the research results of Joshua Abor (2008) and
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 11 www.idpublications.org
Graham et al. (2010). It is also consistent with the findings of Hall et al. (2004) who found
that age is positively related to long-term debt but negatively related to short-term debt.
However, it is not consistent with the results of Michaelas et al. (1999), Esperança et al.
(2003) and Sogorb Mira (2005) who found that age is negatively related to debt, and also the
results of Green et al., (2002) who found that age has a negative influence on debt in the
initial capitalization, and no impact in the additional capitalization.
Change in liquidity position of the firm is also found to have a significant positive
relationship with debt ratio of the firm with level of significance at 1% and P-value of 0.0000.
This implies that liquidity of the firm is a determinant factor of the capital structure of the
firm. Therefore the Fourth hypothesis that there is a statistically significant relationship
between capital structure and liquidity of the firm is accepted. This result is consistent with
the research results of Saurabh and Sharma (2015) who found that liquidity to be empirically
insignificant to determine the capital structure of Indian manufacturing sector. However, it is
not consistent with the results of Gathogo and Ragui (2014) who verified existence of a
negative effect of the “liquidity” of the firms in Kenya on their leverage ratio.
Ownership structure has been found to have negative but insignificant relationship with debt
ratio. This implies that the ownership structure is not a determinant factor of the capital
structure of the firm. Therefore the Fifth hypothesis that there is a statistically significant
relationship between capital structure and ownership structure of the firm is rejected. This
result is consistent with the research results of Amihud et al. (1990) and Zeckhauser and
Pound (1990), who found a negative relationship between the presence of large shareholders
and debt ratio of the firm. However, it is not consistent with the results of Joshua Abor (2008)
who found positive correlation between short term debt ratio and ownership. It also
contradicts with the results of Saurabh and Sharma (2015) who found that ownership
structure is significantly correlated with the firm’s financial leverage or key determinant of
capital structure in Indian manufacturing sector.
The results also show that changes in profits have negatively statistically significant
relationship with debt ratio at 1% level and a p-value of 0.0000. This indicates that as profits
of the firm increases the debt ratio (leverage) decreases. The negative signs indicate that
firms with more profitable projects are inclined to use internally generated funds rather than
debt, and the significance of the coefficients is very striking (Chen and Strange, 2005).
Therefore the Sixth hypothesis that there is a statistically significant relationship between
capital structure and profitability of the firm is accepted. Noticeably, the findings of this
study support the pecking order theory that profitable companies at first rely on cheap
internally generated moneys and afterwards search external sources of financing when there a
need for additional funds. This result is consistent with the research results of Friend and
Lang (1988), Barton et al., (1989), Van der Wijst and Thurik (1993), Chittenden et al. (1996),
Jordan et al. (1998), Shyam-Sunder and Myers (1999), Mishra and McConaughy (1999),
Michaelas et al. (1999), Cassar and Holmes (2003), Esperança et al. (2003), Hall et al.
(2004), Yasir Bin Tariq (2006), Joshua Abor (2008), and Gathogo and Ragui (2014).
However, it is not consistent with the results of Petersen and Rajan (1994) who found a
significantly positive relationship between profitability and leverage.
Change of the size of the firm is also found to have a positively significant relationship with
the debt ratio of the firm. This result shows that larger firms are more likely to borrow to
finance their operations simply because large firms are more diversified and thus have more
stable cash flows, which helps reduce the risk of debt financing. It also indicates that the
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 12 www.idpublications.org
firm’s size is a determinant factor of capital structure of the firm. Therefore the Seventh
hypothesis that there is a statistically significant relationship between capital structure and
size of the firm is accepted. This result is consistent with the research results of (Friend and
Lang (1988), Barton et al. (1989), MacKie-Mason (1990), Barclay and Smith (1996), Kim et
al., (1998), Al-Sakran, (2001), Hovakimian et al., (2004), and Joshua Abor (2008) and
Akinyomi and Olagunju (2013) . However, it is inconsistent with the results of Yasir Bin
Tariq (2006), (1986), Kim and Sorensen (1986), and Titman and Wessels (1988) who found
size to be negatively correlated with capital structure or insignificant factor determining the
capital structure.
The regression results also show that the change in tangibility of assets (assets structure) has
a statistically significant positive relationship to the debt ratio of the firm. This implies that
tangibility of assets is a determinant factor of capital structure of the firm. Therefore, the
Eighth hypothesis that there is a statistically significant relationship between capital structure
and tangibility of assets of the firm is accepted. This result is consistent with the research
results of Bradley et al. (1984) who emphasize that firms that invest heavily in tangible assets
have higher debt ratio (financial leverage) since they might borrow at lower interest rates as
their debts are secured with such tangible assets. It is also consistent with those of Booth et
al. (2001) who states that “the more tangible the firm’s assets, the greater its ability to issue
secured debts” it is also consistent with that of Yasir Bin Tariq (2006). The results also
confirm that of Saurabh and Sharma (2015) and Akinyomi and Olagunju (2013) who found
that asset tangibility is significantly correlated with the firm’s financial leverage. However, it
is not consistent with the results of Booth et al. (2001) and Huang and Song (2002), who
found a negative association between tangibility and debt ratio.
Industry type of the firm is found to have a statistically significant positive relationship with
the debt ratio of the firm with level of significance at 5% and P-value of 0.0486. This implies
that the type of industry is a determinant factor of capital structure of the firm. Therefore, the
Ninth hypothesis that there is a statistically significant relationship between capital structure
and industry type of the firm is accepted. This result is consistent with those of Bradley et al.
(1984), Titman and Wessels (1988), Scherr et al. (1993), and Joshua Abor (2008). It is also
consistent with Hisrich, (1989) and Riding et al. (1994) who found service companies to be
less likely to borrow from banks loans as they seem to have a lack of assets that can be used
as collateral for their loans.
CONCLUSIONS
Capital structure is considered as one of the most discussed issues in financial management.
Capital structure denotes to the way a firm finances its operations as to whether use equity
(common and preferred stocks), debt (bank loans or bonds issuance), or a combination of
both. External as well as internal factors can influence the decision of how the firm finances
its operations. The external factors include, among other things, taxation and macroeconomic
conditions. The internal factors are those that are considered as firm specific (i.e. individual
firm characteristics). This study focused on investigating the internal factors (measures) that
influence the capital structure decision.
This study investigated the determinants of capital structure of a number of companies listed
in the Kuwait Stock Exchange. Data were obtained from 49 selected companies with a total
of 284 observations. The companies were selected from multiple sectors including
manufacturing, services, oil and gas, and basic materials. Some sectors were excluded from
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 13 www.idpublications.org
the analysis as they are considered as highly leveraged or having special characteristics
including financial sector, real estate, and communications. The result of the current study
reveals that capital structure measured by Total Debt to Total Assets has a significant positive
relationship with firm’s age, growth opportunity, liquidity, size, tangibility, and type of
industry. It also reveals that capital structure has significant negative relationships with
profitability. Dividends policy and ownership structure, however, are revealed to have
negative, though, not significant relationship with capital structure. In other words, the study
findings reveal that firm’s age, growth opportunities, liquidity, profitability, firm’s size,
tangibility, and type of industry are determinants of capital structure of Kuwaiti companies.
Dividends policy and ownership structure are revealed to be non-determinants of capital
structure.
This study focuses on some sectors of the Kuwaiti economy. Also the study is restricted to be
based on using only internal factors (individual firm specific). It is suggested to conduct
further researches based on data obtained from all sectors of the economy. This may provide
more evidence on the impact of industry on capital structure determinants. Moreover, the
study suggests using data related to external or macroeconomic factors as measures affecting
the capital structure.
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Appendix Table (1): Study panel data
YEA
R
LEV
R
GRO
P
PRF
T
TAN
G
SIZ
E
OWN
S
FAG
E LIQD
DIV
D
TYP
E RISK
KCEM
2008 0.44 2.4 0.020 0.054
12.3
9 0 40 0.80 1.24 0
2009 0.45 2.3 0.095 0.264
12.4
3 0 41 0.82 0.44 0
0.053
0
2010 0.39 2.6 0.085 0.348
12.4
6 0 42 0.65 0.44 0
0.006
9
2011 0.43 1.9 0.097 0.428
12.4
6 0 43 0.76 0.65 0
0.008
1
2012 0.47 1.8 0.098 0.795
12.5
6 0 44 0.89 0.63 0
0.000
6
2013 0.38 1.3 0.088 0.500
12.6
5 0 45 0.60 0.75 0
0.006
4
REFRI
2008 0.21 0.9
-
0.007 0.374
10.1
9 0 35 0.27 0.00 0
2009 0.22 1.2
-
0.171 0.384
10.0
4 0 36 0.29 0.00 0
0.116
0
2010 0.17 1 0.057 0.333
10.0
3 0 37 0.21 0.40 0
0.160
9
2011 0.23 0.9
-
0.239 0.385 9.87 0 38 0.31 0.00 0
0.209
2
2012 0.25 1.2 0.140 0.391
10.0
5 0 39 0.33 0.00 0
0.268
1
2013 0.34 1.4 0.136 0.302
10.3
2 0 40 0.51 0.33 0
0.002
5
CABLE
2008 0.36 1.4 0.015 0.063
12.3
8 0 33 0.56 4.54 0
2009 0.39 2.2 0.068 0.062
12.4
6 0 34 0.63 1.00 0
0.037
5
2010 0.28 1.8 0.111 0.042
12.7
1 0 35 0.39 0.52 0
0.030
6
2011 0.26 1.7 0.210 0.051
12.3
6 0 36 0.35 0.42 0
0.069
6
2012 0.31 1.7 0.072 0.046
12.3
5 0 37 0.44 0.46 0
0.097
7
2013 0.34 1.2 0.067 0.076
12.3
3 0 38 0.52 0.63 0
0.003
1
SHIP
2008 0.82 3.6 0.035 0.287
11.4
8 0 34 4.48 0.00 0
2009 0.74 3.2 0.239 0.313
11.3
8 0 35 2.81 0.00 0
0.144
3
2010 0.64 2.1 0.163 0.335
11.3
1 0 36 1.75 0.34 0
0.053
6
2011 0.66 1.7 0.113 0.359
11.4
5 0 37 1.95 0.45 0
0.035
2
2012 0.71 0.8 0.051 0.076
11.6
0 0 38 2.39 0.00 0
0.043
8
2013 0.69 0.7 0.055 0.323
11.6
0 0 39 2.22 0.00 0
0.002
2
PCEM
2008 0.14 0.6
-
0.141 0.070
10.9
0 1 32 0.17 -0.24 0
2009 0.11 1.2 0.239 0.058
11.0
4 1 33 0.13 0.68 0
0.268
7
2010 0.11 1.8 0.292 0.032
11.3
6 1 34 0.12 0.37 0
0.037
7
2011 0.07 1 0.040 0.076
11.1
3 1 35 0.07 2.33 0
0.178
4
2012 0.12 1.5 0.115 0.499
11.1
9 1 36 0.13 1.00 0
0.052
7
2013 0.12 1.9 0.120 0.042
11.2
1 1 37 0.13 0.98 0
0.004
1
PAPER
2008 0.16 1.2 0.016 0.391 9.60 1 30 0.19 2.69 0
2009 0.15 0.8 0.127 0.347 9.70 1 31 0.18 0.49 0
0.078
5
2010 0.13 0.8 0.086 0.370 9.69 1 32 0.16 0.45 0
0.029
1
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 18 www.idpublications.org
2011 0.19 0.7 0.070 0.499 9.78 1 33 0.23 0.00 0
0.011
5
2012 0.16 0.7 0.065 0.157 9.83 1 34 0.19 0.59 0
0.003
3
2013 0.16 0.9 0.084 0.452 9.88 1 35 0.19 0.52 0
0.013
6
MRC
2008 0.21 0.6
-
0.185 0.130
10.4
0 0 21 0.28 0.00 0
2009 0.25 0.5
-
0.178 0.166
10.2
6 0 22 0.35 0.00 0
0.005
0
2010 0.2 0.6
-
0.026 0.172
10.1
5 0 23 0.26 0.00 0
0.107
7
2011 0.16 0.5
-
0.011 0.157
10.1
1 0 24 0.21 0.00 0
0.010
2
2012 0.14 0.5
-
0.086 0.132
10.0
0 0 25 0.18 0.00 0
0.053
1
2013 0.16 0.6
-
0.054 0.139 9.98 0 26 0.21 0.00 0
0.023
0
ACICO
2008 0.63 1.3 0.089 0.159
12.2
2 0 18 1.80 0.16 0
2009 0.63 1.1 0.051 0.148
12.2
8 0 19 1.76 0.57 0
0.026
9
2010 0.66 1 0.049 0.132
12.3
9 0 20 2.04 0.59 0
0.001
8
2011 0.66 0.7 0.025 0.132
12.3
9 0 21 2.06 0.62 0
0.016
9
2012 0.66 0.7 0.033 0.398
12.4
0 0 22 2.00 0.70 0
0.006
1
2013 0.65 0.9 0.076 0.165
12.4
3 0 23 1.92 0.39 0
0.030
4
GGMC
2008 0.39 1.5 0.220 0.526 9.63 1 27 0.64 0.41 0
2009 0.25 2.4 0.294 0.491 9.64 1 28 0.33 0.38 0
0.052
3
2010 0.11 2 0.221 0.481 9.61 1 29 0.12 0.52 0
0.051
8
2011 0.1 3.4 0.178 0.398 9.68 1 30 0.11 0.63 0
0.030
4
2012 0.1 1.8 0.170 0.244 9.74 1 31 0.11 0.70 0
0.005
4
2013 0.09 1.8 0.129 0.529 9.75 1 32 0.10 0.63 0
0.028
9
HCC
2008 0.29 1.4 0.045 0.310
10.0
3 0 24 0.43 0.00 0
2009 0.27 1.1 0.002 0.300 9.98 0 25 0.38 0.00 0
0.030
4
2010 0.25 1.1 0.045 0.273 9.99 0 26 0.34 0.98 0
0.030
4
2011 0.24 0.9 0.074 0.244
10.0
2 0 27 0.33 0.86 0
0.020
7
2012 0.24 0.8 0.032 0.596
10.0
0 0 28 0.34 2.09 0
0.030
2
2013 0.26 1 0.034 0.259
10.0
0 0 29 0.38 0.94 0
0.001
6
KPAK
2008 0.12 2.8 0.160 0.576 9.25 0 9 0.13 0.73 0
2009 0.08 2.6 0.154 0.582 9.28 0 10 0.09 0.58 0
0.004
2
2010 0.12 1.5 0.098 0.526 9.33 0 11 0.14 0.88 0
0.040
0
2011 0.12 1.1 0.199 0.596 9.45 0 12 0.14 0.00 0
0.071
8
2012 0.07 0.8 0.108 0.537 9.52 0 13 0.07 0.62 0
0.064
2
2013 0.12 1.4 0.139 0.492 9.65 0 14 0.13 0.44 0
0.021
6
KBMMC 2008 0.18 1.5 0.123 0.423 8.62 1 32 0.22 0.72 0
2009 0.12 2 0.122 0.467 8.59 1 33 0.14 0.70 0
0.000
7
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2010 0.3 1.7 0.043 0.515 8.78 1 34 0.43 0.00 0
0.055
9
2011 0.26 1.4 0.039 0.537 8.76 1 35 0.35 0.79 0
0.002
8
2012 0.23 1.1 0.083 0.244 8.78 1 36 0.30 0.35 0
0.031
0
2013 0.16 1.6 0.107 0.479 8.77 1 37 0.19 0.52 0
0.017
5
NICBM
2008 0.44 1.9
-
0.112 0.153
11.7
4 0 10 0.78 0.00 0
2009 0.4 2
-
0.037 0.188
11.7
0 0 12 0.66 0.00 0
0.053
0
2010 0.29 1.7 0.052 0.222
11.6
5 0 13 0.42 0.68 0
0.062
6
2011 0.27 1.2 0.054 0.244
11.6
0 0 14 0.37 0.81 0
0.001
9
2012 0.27 1.5 0.062 0.272
11.6
2 0 15 0.35 0.83 0
0.005
5
2013 0.22 0.8 0.006 0.278
11.5
8 0 16 0.30 0.00 0
0.039
3
EQUIPMENT
2008 0.67 0.9
-
0.520 0.121
10.6
5 0 9 2.11 0.00 0
2009 0.65 0.6
-
0.866 0.118
10.5
4 0 10 1.90 0.00 0
0.244
7
2010 0.62 0.6 0.032 0.268
10.6
1 0 11 1.67 0.00 0
0.635
2
2011 0.65 0.5
-
0.071 0.272
10.6
4 0 12 1.91 0.00 0
0.073
2
2012 0.62 1.3
-
0.110 0.028
10.6
0 0 13 1.77 0.00 0
0.027
4
2013 0.45 1.1 0.166 0.337
10.3
9 0 14 0.83 0.00 0
0.195
2
NCCI
2008 0.04 1.1
-
0.019 0.017 9.20 0 12 0.04 0.00 0
2009 0.04 1.1
-
0.007 0.021 9.20 0 13 0.04 0.00 0
0.008
5
2010 0.05 0.9
-
0.059 0.027 9.14 0 14 0.06 0.00 0
0.036
4
2011 0.06 1.2
-
0.233 0.028 8.93 0 15 0.06 0.00 0
0.123
1
2012 0.07 1.3
-
0.007 0.719 8.94 0 16 0.07 0.00 0
0.159
2
2013 0.14 1.8
-
0.025 0.049 8.99 0 17 0.16 0.00 0
0.012
5
GYPSUM
2008 0.06 1.1 0.122 0.552 8.51 1 27 0.06 0.78 0
2009 0.24 1.3 0.123 0.519 8.75 1 28 0.32 0.77 0
0.000
7
2010 0.21 1.1 0.091 0.579 8.72 1 29 0.27 0.83 0
0.023
0
2011 0.2 0.8 0.044 0.719 8.67 1 30 0.25 1.03 0
0.033
0
2012 0.15 0.8 0.068 0.169 8.63 1 31 0.18 0.93 0
0.016
9
2013 0.15 1.2 0.000 0.736 8.56 1 32 0.17 0.00 0
0.047
9
SALBOOKH
2008 0.3 1.5 0.022 0.188
10.3
1 1 35 0.43 0.00 0
2009 0.2 0.6
-
0.112 0.406
10.4
4 1 36 0.26 0.00 0
0.094
8
2010 0.32 1.2
-
0.557 0.276 9.79 1 37 0.47 0.00 0
0.314
9
2011 0.36 0.9
-
0.148 0.169 9.69 1 38 0.57 0.00 0
0.289
6
2012 0.37 0.8
-
0.120 0.144 9.60 1 39 0.59 0.00 0
0.019
4
2013 0.31 1.2 0.003 0.089 9.51 1 40 0.45 0.00 0
0.087
0
AGLTY 2008 0.52 0.8 0.187 0.135
14.3
1 0 29 1.13 0.00 1
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
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2009 0.47 0.6 0.184 0.144
14.3
9 0 30 0.88 0.26 1
0.002
1
2010 0.38 0.6 0.027 0.159
14.2
2 0 31 0.63 1.61 1
0.111
0
2011 0.36 0.4 0.030 0.144
14.1
5 0 32 0.56 1.11 1
0.002
3
2012 0.38 0.6 0.038 0.158
14.1
8 0 33 0.58 0.87 1
0.005
7
2013 0.37 0.8 0.053 0.121
14.1
6 0 34 0.59 0.90 1
0.010
2
EDU
2008 0.42 0.5 0.133 0.057
11.4
3 0 26 0.74 0.00 1
2009 0.46 0.7
-
0.410 0.216
11.1
2 0 27 0.86 0.00 1
0.384
0
2010 0.44 0.6
-
0.191 0.167
11.0
1 0 28 0.94 0.00 1
0.154
9
2011 0.45 1 0.016 0.158
11.0
2 0 29 0.98 0.00 1
0.146
7
2012 0.43 0.8 0.017 0.257
10.9
9 0 30 0.90 1.48 1
0.000
6
2013 0.39 1.2 0.128 0.054
11.0
2 0 31 0.75 0.36 1
0.078
2
CLEANING
2008 0.62 1.1
-
0.114 0.337
11.1
2 0 30 2.17 0.00 1
2009 0.65 0.6 0.046 0.322
11.0
0 0 31 1.94 0.00 1
0.113
1
2010 0.51 0.7 0.066 0.340
10.7
4 0 32 1.08 0.00 1
0.014
1
2011 0.36 0.7 0.049 0.257
10.8
8 0 33 0.58 0.63 1
0.012
3
2012 0.57 0.8 0.103 0.674
11.3
4 0 34 1.34 0.00 1
0.038
7
2013 0.57 0.9 0.052 0.465
11.4
2 0 35 1.38 0.00 1
0.036
5
CITYGROUP
2008 0.16 2.7 0.097 0.343
10.3
1 0 31 0.19 1.01 1
2009 0.2 2.7 0.117 0.744
10.3
7 0 32 0.26 0.00 1
0.014
1
2010 0.36 2.5 0.049 0.617
10.9
7 0 33 0.80 0.00 1
0.048
1
2011 0.32 2.8
-
0.260 0.674
10.3
5 0 34 0.48 0.00 1
0.218
3
2012 0.25 2.3 0.145 0.457
10.4
0 0 35 0.33 1.11 1
0.286
0
2013 0.23 2.2 0.208 0.515
10.4
4 0 36 0.30 0.72 1
0.044
9
KGL
2008 0.69 0.4
-
0.111 0.557
12.3
9 1 26 2.34 0.00 1
2009 0.68 0.5 0.009 0.527
12.3
3 1 27 2.21 0.00 1
0.084
9
2010 0.67 0.6
-
0.066 0.500
12.2
5 1 28 2.14 0.00 1
0.053
0
2011 0.7 0.5
-
0.164 0.457
12.2
1 1 29 2.40 0.00 1
0.068
9
2012 0.71 0.5
-
0.136 0.225
12.1
1 1 30 2.55 0.00 1
0.019
4
2013 0.73 0.5 0.003 0.351
12.1
6 1 31 2.75 0.00 1
0.098
4
KCPC
2008 0.7 1.1 0.166 0.115
10.4
6 0 29 2.36 0.13 1
2009 0.64 0.9 0.160 0.189
10.5
6 0 30 1.74 0.13 1
0.004
2
2010 0.54 1.7 0.087 0.229
10.3
8 0 31 1.18 0.22 1
0.051
6
2011 0.53 1.1 0.112 0.225
10.4
7 0 32 1.13 0.33 1
0.017
4
2012 0.55 1.1 0.104 0.498
10.5
7 0 33 1.16 0.35 1
0.005
6
2013 0.57 1.1 0.099 0.210 10.8 0 34 1.35 0.32 1 0.003
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4 7
HUMANSOF
T
2008 0.25 1.8 0.020 0.386
10.0
5 0 11 0.34 0.00 1
2009 0.29 1.9 0.049 0.444
10.1
5 0 12 0.40 0.00 1
0.020
5
2010 0.37 1.9 0.012 0.522
10.2
6 0 13 0.59 0.00 1
0.026
2
2011 0.35 1.5 0.112 0.498
10.4
0 0 14 0.53 0.00 1
0.070
4
2012 0.41 1.4 0.102 0.195
10.6
0 0 15 0.69 0.70 1
0.006
6
2013 0.39 1 0.189 0.478
10.7
0 0 16 0.63 0.78 1
0.061
2
NAFAIS
2008 0.33 1.1 0.030 0.260
11.6
9 1 24 0.53 0.00 1
2009 0.6 1.3
-
0.431 0.244
11.8
5 1 25 1.61 0.00 1
0.326
0
2010 0.52 0.8
-
0.182 0.150
11.5
7 1 26 1.25 0.00 1
0.176
1
2011 0.41 0.9 0.038 0.195
11.2
9 1 27 0.68 0.00 1
0.155
8
2012 0.37 0.7 0.054 0.048
11.2
9 1 28 0.58 0.00 1
0.010
8
2013 0.32 0.7 0.102 0.170
11.3
1 1 29 0.48 0.00 1
0.033
9
SAFWAN
2008 0.58 1.3 0.241 0.050 9.88 0 8 1.39 0.52 1
2009 0.57 1.2 0.194 0.046 9.93 0 9 1.31 0.60 1
0.033
2
2010 0.6 1.5 0.198 0.046
10.0
9 0 10 1.51 0.59 1
0.002
8
2011 0.64 2.6 0.198 0.048
10.2
8 0 11 1.80 0.64 1
0.000
3
2012 0.65 2.1 0.206 0.102
10.3
9 0 12 1.85 0.62 1
0.005
7
2013 0.7 1.8 0.210 0.077
10.6
5 0 13 2.35 0.61 1
0.003
2
GFC
2008 0.43 1
-
0.334 0.058 9.95 0 7 0.76 0.00 1
2009 0.42 0.7
-
0.535 0.112 9.38 0 8 0.71 0.00 1
0.142
1
2010 0.42 0.8
-
0.323 0.099 9.03 0 9 0.73 0.00 1
0.149
9
2011 0.45 0.7
-
0.474 0.102 8.68 0 10 0.82 0.00 1
0.107
1
2012 0.42 0.9
-
0.073 0.015 8.56 0 11 0.72 0.00 1
0.284
2
2013 0.42 1
-
0.040 0.048 8.52 0 12 0.73 0.00 1
0.022
9
MAYADEEN
2008 0.48 0.8
-
0.320 0.016
11.7
6 0 10 0.94 0.00 1
2009 0.49 0.5
-
0.021 0.014
11.8
1 0 11 0.98 0.00 1
0.211
4
2010 0.57 0.3
-
0.263 0.015
11.6
9 0 12 1.34 0.00 1
0.171
1
2011 0.7 0.4
-
0.382 0.015
11.5
9 0 13 2.38 0.00 1
0.084
1
2012 0.58 0.5
-
0.020 0.116
11.2
8 0 14 1.39 0.00 1
0.256
2
2013 0.56 0.7
-
0.056 0.018
11.2
4 0 15 1.29 0.00 1
0.026
0
CGC
2008 0.78 1.4 0.333 0.119
11.7
4 1 43 3.59 0.51 1
2009 0.73 3.4 0.280 0.128
11.6
7 1 44 2.73 0.63 1
0.037
5
2010 0.73 4.9 0.264 0.108
11.8
2 1 45 2.74 0.69 1
0.011
3
2011 0.72 3.8 0.253 0.116
11.8
9 1 46 2.62 0.67 1
0.008
0
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2012 0.74 3.6 0.240 0.055
12.0
6 1 47 3.01 0.72 1
0.009
1
2013 0.78 3.4 0.117 0.093
12.1
7 1 48 3.76 0.80 1
0.087
3
MTCC
2008 0.77 1.5
-
0.477 0.112
11.5
1 0 40 3.43 0.00 1
2009 0.74 1.4 0.034 0.087
11.4
6 0 41 2.90 0.00 1
0.361
3
2010 0.68 1 0.063 0.068
11.2
6 0 42 2.08 0.00 1
0.020
5
2011 0.64 0.8 0.074 0.055
11.2
1 0 43 1.74 0.00 1
0.007
8
2012 0.62 0.5 0.055 0.016
11.2
5 0 44 1.66 0.00 1
0.013
2
2013 0.68 0.7 0.049 0.051
11.4
8 0 45 2.15 0.00 1
0.004
5
UPAC
2008 0.27 1 0.059 0.017
10.6
7 0 8 0.40 0.69 1
2009 0.25 0.6 0.134 0.019
10.7
2 0 9 0.36 0.69 1
0.053
0
2010 0.2 1.1 0.104 0.016
10.6
7 0 10 0.27 0.72 1
0.021
2
2011 0.19 0.8 0.072 0.016
10.6
4 0 11 0.25 1.04 1
0.022
3
2012 0.16 1.3 0.205 0.916
10.7
4 0 12 0.20 2.10 1
0.093
4
2013 0.19 2.3 0.247 0.007
10.5
1 0 13 0.25 0.91 1
0.030
1
ALAFCO
2008 0.69 1 0.126 0.714
12.4
7 0 8 2.22 0.33 1
2009 0.76 1.5 0.117 0.768
12.8
7 0 9 3.17 0.00 1
0.006
4
2010 0.81 2.3 0.110 0.880
13.1
9 0 10 4.21 0.35 1
0.005
0
2011 0.76 1.7 0.328 0.916
13.2
7 0 11 3.08 0.17 1
0.154
4
2012 0.72 1.7 0.157 0.409
13.2
7 0 12 2.55 0.15 1
0.121
1
2013 0.73 1.2 0.114 0.846
13.4
1 0 13 2.70 0.19 1
0.030
3
MUBARRAD
2008 0.2 0.6 0.156 0.326
10.5
5 0 12 0.25 0.49 1
2009 0.28 0.6
-
0.304 0.356
10.2
9 0 13 0.38 0.00 1
0.325
3
2010 0.25 0.8
-
0.008 0.432
10.4
7 0 14 0.33 0.00 1
0.209
3
2011 0.22 0.5
-
0.020 0.409
10.4
0 0 15 0.27 0.00 1
0.008
8
2012 0.29 0.7
-
0.560 0.165
10.0
5 0 16 0.40 0.00 1
0.381
2
2013 0.13 0.7 0.037 0.139 9.90 0 17 0.15 0.00 1
0.421
9
LOGISTICS
2008 0.44 N/A 0.075 0.036
10.9
9 0 7 0.79 0.00 1
2009 0.36 1 0.139 0.031
10.9
9 0 8 0.57 0.00 1
0.045
3
2010 0.31 1.5 0.181 0.030
11.0
9 0 9 0.45 0.15 1
0.029
7
2011 0.23 1.3 0.173 0.048
11.1
3 0 10 0.30 0.61 1
0.006
0
2012 0.14 1.5 0.177 0.467
11.1
0 0 11 0.16 0.60 1
0.003
0
2013 0.1 1.5 0.049 0.061
11.1
2 0 12 0.12 0.37 1
0.090
0
SCEM
2008 0.29 0.9 0.166 0.425
11.9
3 0 32 0.41 0.50 0
2009 0.24 0.6 0.071 0.480
11.8
9 0 33 0.31 0.50 0
0.067
2
2010 0.24 0.5 0.025 0.473 11.8 0 34 0.32 0.78 0 0.032
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6 5
2011 0.25 0.2
-
0.035 0.467
11.8
2 0 35 0.33 -0.54 0
0.042
3
2012 0.25 0.4 0.018 0.376
11.8
1 0 36 0.34 1.14 0
0.037
6
2013 0.24 0.5 0.034 0.430
11.8
8 0 37 0.32 0.85 0
0.011
2
GCEM
2008 0.14 1.2 0.001 0.344
11.8
2 0 31 0.16 58.13 0
2009 0.1 0.9 0.024 0.351
11.7
7 0 32 0.12 2.27 0
0.016
3
2010 0.08 0.9 0.050 0.327
11.7
1 0 33 0.08 1.12 0
0.018
4
2011 0.08 0.5
-
0.045 0.376
11.5
4 0 34 0.08 0.00 0
0.066
8
2012 0.11 0.7
-
0.006 0.047
11.5
6 0 35 0.13 -6.10 0
0.027
6
2013 0.19 0.9 0.056 0.502
11.6
6 0 36 0.23 0.60 0
0.043
2
QCEM
2008 0.11 0.4 0.012 0.049
11.0
3 0 26 0.12 0.00 0
2009 0.06 0.4
-
0.080 0.050
10.9
2 0 27 0.06 0.00 0
0.065
1
2010 0.05 0.5 0.010 0.051
10.7
2 0 28 0.05 0.00 0
0.063
6
2011 0.05 0.4 0.020 0.047
10.6
2 0 29 0.05 2.46 0
0.007
3
2012 0.06 0.6 0.020 0.738
10.6
0 0 30 0.06 2.21 0
0.000
3
2013 0.07 0.7 0.037 0.021
10.9
2 0 31 0.07 1.04 0
0.011
9
FCEM
2008 0.41 1.3 0.223 0.649
11.7
0 0 29 0.69 0.16 0
2009 0.41 0.8 0.083 0.781
11.7
8 0 30 0.69 0.26 0
0.099
0
2010 0.45 0.4 0.005 0.787
11.8
2 0 31 0.83 0.00 0
0.055
2
2011 0.5 0.2
-
0.078 0.738
11.8
3 0 32 0.99 0.00 0
0.058
5
2012 0.47 0.4 0.038 0.476
11.8
2 0 33 0.90 0.00 0
0.081
5
2013 0.46 0.4
-
0.013 0.725
11.7
9 0 34 0.86 0.00 0
0.035
9
RKWC
2008 0.15 0.5
-
0.091 0.040
11.0
4 0 28 0.18 0.00 0
2009 0.13 0.6 0.098 0.160
11.0
8 0 29 0.15 0.80 0
0.133
6
2010 0.16 0.8 0.110 0.385
11.1
7 0 30 0.19 0.68 0
0.008
5
2011 0.24 1.1 0.097 0.500
11.1
7 0 31 0.32 0.85 0
0.008
9
2012 0.25 0.8 0.052 0.500
11.2
2 0 32 0.33 1.24 0
0.032
5
2013 0.29 0.8 0.058 0.434
11.4
7 0 33 0.41 0.46 0
0.004
2
NIND
2008 0.72 1.5
-
0.440 0.023
14.3
7 1 48 3.59 0.00 1
2009 0.65 1
-
0.062 0.031
14.2
7 1 49 2.57 0.00 1
0.267
3
2010 0.61 0.9
-
0.044 0.041
14.3
1 1 50 2.15 0.00 1
0.012
7
2011 0.65 0.9
-
0.076 0.044
14.2
2 1 51 2.59 0.00 1
0.022
8
2012 0.62 0.7 0.034 N/A
14.1
4 1 52 2.26 0.00 1
0.078
3
2013 0.59 0.7 0.026 0.051
14.1
4 1 53 1.96 0.00 1
0.006
3
PIPE 2008 0.78 0.7
-
0.722 0.177
12.2
0 0 42 4.19 0.00 0
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 24 www.idpublications.org
2009 0.84 1.3
-
0.151 0.180
12.3
3 0 43 5.93 0.00 0
0.403
8
2010 0.85 1.9 0.012 0.178
12.3
8 0 44 6.37 0.00 0
0.115
3
2011 0.9 1.4
-
0.498 0.204
12.3
5 0 45 9.75 0.00 0
0.360
5
2012 0.9 1.7
-
0.460 0.257
12.1
5 0 46 8.93 0.00 0
0.026
5
2013 0.99 -9.4 7.808 0.554
12.1
4 0 47
148.1
7 0.00 0
5.846
6
MARIN
2008 0.55 2.5 0.027 0.757
11.7
4 1 35 1.86 0.00 1
2009 0.56 2.1 0.087 0.797
12.0
5 1 36 2.40 0.00 1
0.042
4
2010 0.55 1.6 0.049 0.755
12.1
3 1 37 2.40 0.49 1
0.026
9
2011 0.54 1.1 0.039 0.777
12.1
3 1 38 2.35 0.00 1
0.007
2
2012 0.52 0.9 0.041 0.790
12.1
2 1 39 2.17 0.57 1
0.001
6
2013 0.57 0.9 0.037 0.839
12.2
4 1 40 2.62 0.00 1
0.003
1
KFOUC
2008 0.07 2.8
-
0.068 0.010
10.7
6 1 35 0.08 0.00 0
2009 0.03 1.7 0.148 0.009
10.8
3 1 36 0.03 0.32 0
0.152
7
2010 0.03 1.6 0.102 0.008
10.9
9 1 37 0.03 0.41 0
0.032
8
2011 0.04 1.3
-
0.068 0.009
10.8
4 1 38 0.05 0.00 0
0.119
9
2012 0.03 0.9 0.021 0.008
10.8
8 1 39 0.03 0.67 0
0.063
1
2013 0.03 1.1 0.041 0.007
10.9
4 1 40 0.03 0.69 0
0.013
7
UIC
2008 0.59 0.3
-
0.139 0.001
12.2
0 1 29 1.64 0.00 0
2009 0.46 0.3
-
0.105 0.001
11.7
7 1 30 0.86 0.00 0
0.024
0
2010 0.42 0.6 0.049 0.000
12.0
9 1 31 0.78 0.00 0
0.108
9
2011 0.42 0.5 0.013 0.000
12.0
9 1 32 0.77 0.00 0
0.025
8
2012 0.48 0.5 0.034 1.012
12.0
3 1 33 0.82 0.68 0
0.015
2
2013 0.25 0.5 0.220 0.949
12.0
4 1 34 0.33 0.40 0
0.131
2
BPCC
2008 0.44 0.8 0.081 0.025
12.9
0 0 13 0.78 0.59 0
2009 0.43 0.8 0.091 0.034
12.9
8 0 14 0.77 0.68 0
0.007
1
2010 0.4 1 0.091 0.036
12.9
7 0 15 0.66 0.74 0
0.000
0
2011 0.35 1 0.088 0.048
12.9
7 0 16 0.53 0.74 0
0.001
8
2012 0.32 1 0.089 0.050
12.9
3 0 17 0.47 0.78 0
0.000
2
2013 0.27 1.1 0.095 0.069
12.8
9 0 18 0.37 0.79 0
0.004
6
ALKOUT
2008 0.49 2.6 0.151 0.296
10.4
1 1 15 0.95 0.00 0
2009 0.38 1.7 0.133 0.308
10.3
5 1 16 0.95 0.55 0
0.012
7
2010 0.21 1.6 0.137 0.365
10.1
7 1 17 0.27 0.64 0
0.002
8
2011 0.29 1.4 0.162 0.408
10.3
7 1 18 0.41 0.84 0
0.017
6
2012 0.3 1.7 0.183 0.391
10.4
4 1 19 0.42 0.80 0
0.014
8
2013 0.24 1.9 0.204 0.156 10.4 1 20 0.31 0.67 0 0.015
European Journal of Business, Economics and Accountancy Vol. 3, No. 6, 2015 ISSN 2056-6018
Progressive Academic Publishing, UK Page 25 www.idpublications.org
3 2
ALQURAIN
2008 0.02 1.3
-
0.038 0.000
11.8
6 0 4 0.02 0.00 0
2009 0.06 1.1 0.045 0.000
12.1
6 0 5 0.07 0.00 0
0.058
7
2010 0.01 1.1
-
0.010 0.000
12.1
7 0 6 0.01 0.00 0
0.038
9
2011 0.01 0.9 0.131 0.000
12.4
5 0 7 0.01 0.50 0
0.099
4
2012 0.02 0.7 0.078 0.000
12.5
8 0 8 0.02 0.49 0
0.037
1
2013 0.15 0.8 0.091 0.075
12.8
1 0 9 0.17 0.38 0
0.009
1
IKARUS
2008 0.44 1.6
-
0.392 0.000
11.6
2 1 12 0.78 0.00 0
2009 0.24 0.8
-
0.011 0.000
11.9
2 1 13 0.32 0.00 0
0.269
4
2010 0.44 0.8 0.034 0.000
11.3
1 1 14 0.25 0.00 0
0.031
8
2011 0.18 0.9 0.056 0.000
12.1
1 1 15 0.22 0.71 0
0.015
8
2012 0.22 0.9 0.068 0.000
12.0
4 1 16 0.29 1.00 0
0.008
1
2013 0.16 0.7 0.064 0.000
12.2
4 1 17 0.19 0.67 0
0.002
8
BIIHC
2008 N/A N/A N/A N/A N/A 1 4 N/A N/A 1
2009 0.32 N/A 0.057 0.017
11.2
6 1 5 N/A 0.00 1
2010 0.29 0.7 0.068 0.016
11.2
0 1 6 0.41 0.00 1
0.007
8
2011 0.23 0.5 0.004 0.292
11.0
9 1 7 0.30 0.00 1
0.045
3
2012 0.19
-
0.246 0.766
10.7
9 1 8 0.23 0.00 1
0.176
6
2013 0.07
-
0.170 0.850
10.5
3 1 9 0.07 0.00 1
0.053
5